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Record W2147477446 · doi:10.1586/14737175.2014.887443

The impact of post-stroke spasticity and botulinum toxin on standing balance: a systematic review

2014· review· en· W2147477446 on OpenAlexaff
Chetan P. Phadke, Farooq Ismail, Chris Boulias, William H. Gage, George Mochizuki

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2014
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsHeart and Stroke FoundationSunnybrook Health Science CentreToronto Rehabilitation InstituteYork UniversityUniversity of TorontoWest Park Healthcare Centre
Fundersnot available
KeywordsSpasticityBalance (ability)Botulinum toxinPhysical medicine and rehabilitationMedicineStroke (engine)Physical therapyAnesthesia

Abstract

fetched live from OpenAlex

Although falls, balance impairment, and spasticity are common post-stroke, their interrelationship remains unclear. We review the literature for a) a relationship between spasticity and balance and b) the effect of botulinum toxin injections on balance. Electronic databases were searched based on two criteria: a) studies assessing balance in subjects with spasticity and b) studies examining the effect of botulinum toxin on balance. The primary findings were a) balance is impaired in subjects with spasticity, but only one study assessed relationship between spasticity and balance; and b) four studies reported that balance improves following botulinum treatment for limb spasticity. Persons with spasticity demonstrate impaired balance, but the correlation between spasticity and balance has not been adequately assessed in the literature. Evidence for balance changes following botulinum toxin is weak because of lack of randomization, control group comparison, objective balance assessment measures, and standard clinical scales.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0080.009
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.389
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2014
Admission routes1
Has abstractyes

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